Cluster Language Model for Improved E-Commerce Retrieval and Ranking: Leveraging Query Similarity and Fine-Tuning for Personalized Results

Duleep Rathgamage Don, Ying Xie, Le Yu, Simon Hughes, Yun Zhu

Abstract
This paper proposes a novel method to improve the accuracy of product search in e-commerce by utilizing a cluster language model. The method aims to address the limitations of the bi-encoder architecture while maintaining a minimal additional training burden. The approach involves labeling top products for each query, generating semantically similar query clusters using the K-Means clustering algorithm, and fine-tuning a global language model into cluster language models on individual clusters. The parameters of each cluster language model are fine-tuned to learn local manifolds in the feature space efficiently, capturing the nuances of various query types within each cluster. The inference is performed by assigning a new query to its respective cluster and utilizing the corresponding cluster language model for retrieval. The proposed method results in more accurate and personalized retrieval results, offering a superior alternative to the popular bi-encoder based retrieval models in semantic search.
Anthology ID:
2024.ecnlp-1.15
Volume:
Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Shervin Malmasi, Besnik Fetahu, Nicola Ueffing, Oleg Rokhlenko, Eugene Agichtein, Ido Guy
Venues:
ECNLP | WS
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
145–153
Language:
URL:
https://aclanthology.org/2024.ecnlp-1.15
DOI:
Bibkey:
Cite (ACL):
Duleep Rathgamage Don, Ying Xie, Le Yu, Simon Hughes, and Yun Zhu. 2024. Cluster Language Model for Improved E-Commerce Retrieval and Ranking: Leveraging Query Similarity and Fine-Tuning for Personalized Results. In Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024, pages 145–153, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Cluster Language Model for Improved E-Commerce Retrieval and Ranking: Leveraging Query Similarity and Fine-Tuning for Personalized Results (Rathgamage Don et al., ECNLP-WS 2024)
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